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EEG-based predictors of motor recovery during immersive VR-BCI rehabilitation
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  • Published: 09 February 2026

EEG-based predictors of motor recovery during immersive VR-BCI rehabilitation

  • Madalena Valente1,
  • Diogo Branco2,3,
  • Sergi Bermúdez i Badia2,3,
  • Jean-Claude Fernandes1,4,
  • Patrícia Figueiredo1 &
  • …
  • Athanasios Vourvopoulos1 

Scientific Reports , Article number:  (2026) Cite this article

  • 588 Accesses

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Neurology
  • Neuroscience

Abstract

Motor impairment following stroke frequently leads to long-term disability, limiting independence and quality of life. Brain–Computer Interface (BCI) systems integrating motor imagery (MI) with virtual reality (VR) offer promising avenues for enhancing neuroplasticity and engagement through immersive, real-time, and proprioceptive feedback. Yet, identifying reliable electroencephalography (EEG)-based biomarkers that reflect or predict recovery remains challenging. This study investigated the relationship between event-related desynchronization (ERD) dynamics during MI–VR training and motor recovery in individuals with chronic stroke. Fourteen participants with stroke (9 experimental, 5 control) completed a 4-week VR–BCI intervention and were compared with a non-stroke reference cohort (N = 35). Linear mixed-effects models assessed ERD modulation across sessions and groups, and a two-stage regression evaluated the predictive value of ERD features for Fugl–Meyer Assessment (FMA) gains. Results showed no significant ERD change across sessions, but stroke participants exhibited significantly reduced ERD compared to controls. Baseline ERD amplitude predicted motor improvement, whereas ERD progression did not. Ipsilateral ERD showed a compensatory trend in ischemic stroke. These findings indicate that baseline ERD may serve as a stronger prognostic biomarker than short-term ERD dynamics, supporting the development of personalized VR–BCI rehabilitation strategies for chronic stroke recovery.

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Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

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Acknowledgements

This work is supported by the LARSyS - FCT Project (DOI: 10.54499/LA/P/0083/2020, 10.54499/UIDP/50009/2020, and 10.54499/UIDB/50009/2020), the NeurAugVR (PTDC/CCI-COM/31485/2017), the NOISyS project (DOI: 10.54499/2022.02283.PTDC), the NOVA LINCS (DOI: 10.54499/UIDB/04516/2020 and 10.54499/UIDP/04516/2020) with the financial support of FCT.IP (2021.05646.BD) and the Recovery and Resilience Plan under the application no 761 submitted to the measure Polos de Inovação Digital (DIH) under the terms of AAC no. 03/C16 i03/2022. Finally, we would like to acknowledge Audrey Aldridge, Carolina Jorge, Diego Andres Blanco-Mora, Sofia Ferreira, Mónica Rosa, and Sidonio Fernandes for assisting with the patient’s preparation and data acquisition at the hospital.

Funding

This work is supported financially by FCT through the LARSyS - FCT Project (DOI: 10.54499/LA/P/0083/2020, 10.54499/UIDP/50009/2020, and 10.54499/UIDB/50009/2020), the NeurAugVR (PTDC/CCI-COM/31485/2017), the NOISyS project (DOI: 10.54499/2022.02283.PTDC), the FCT grant: 10.54499/2021.05646.BD, the NOVA LINCS (DOI: 10.54499/UIDB/04516/2020 and 10.54499/UIDP/04516/2020) with the financial support of FCT.IP (2021.05646.BD) and the Recovery and Resilience Plan under the application no 761 submitted to the measure Polos de Inovação Digital (DIH) under the terms of AAC no. 03/C16 i03/2022.

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Authors and Affiliations

  1. Bioengineering Department, Institute for Systems and Robotics - Lisboa, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    Madalena Valente, Jean-Claude Fernandes, Patrícia Figueiredo & Athanasios Vourvopoulos

  2. Faculty of Exact Sciences and Engineering & NOVA LINCS, University of Madeira, Funchal, Portugal

    Diogo Branco & Sergi Bermúdez i Badia

  3. ARDITI - Agência Regional para o Desenvolvimento da Investigação, Tecnologia e Inovação, Funchal, Portugal

    Diogo Branco & Sergi Bermúdez i Badia

  4. Physical Medicine and Rehabilitation Service, Central Hospital of Funchal, Funchal, Portugal

    Jean-Claude Fernandes

Authors
  1. Madalena Valente
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  2. Diogo Branco
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  3. Sergi Bermúdez i Badia
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  4. Jean-Claude Fernandes
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  5. Patrícia Figueiredo
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  6. Athanasios Vourvopoulos
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Contributions

CRediT taxonomy: MV: Data curation; Formal analysis; Investigation; Visualization; Writing - original draft DB: Data curation; Investigation; Validation; Writing - review & editing. JC-F: Data curation; Investigation; Validation; Writing - review & editing. SBB: Conceptualization; Investigation; Funding acquisition; Project Administration; Resources; Writing - review & editing. PF: Conceptualization; Investigation; Funding acquisition; Resources; Supervision; Validation; Writing - review & editing. AV: Conceptualization; Investigation; Software; Methodology; Supervision; Resources; Validation; Writing - review & editing.

Corresponding author

Correspondence to Athanasios Vourvopoulos.

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Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study was performed in accordance with the Declaration of Helsinki. This human study was approved by Scientific and Ethic Committees of the Central Hospital of Funchal, Portugal - approval: 21/2019. The study’s clinical trial registration number is NCT04376138 registered with https://clinicaltrials.gov/study/NCT04376138. Participant registration took place from Aug-2019 to Dec-2023. All adult participants provided written informed consent to participate in this study.

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All participants provided written informed consent for the publication of anonymized data included in this manuscript.

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Valente, M., Branco, D., Bermúdez i Badia, S. et al. EEG-based predictors of motor recovery during immersive VR-BCI rehabilitation. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39106-1

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  • Received: 11 December 2025

  • Accepted: 03 February 2026

  • Published: 09 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-39106-1

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Keywords

  • Brain-Computer Interfaces
  • Motor Imagery
  • Event-Related Desynchronization
  • Stroke Rehabilitation
  • Virtual Reality
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